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Articles

Cross-project utilisation of tunnel boring machine (TBM) construction data: a case study using big data from Yin-Song diversion project in China

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Pages 127-147 | Received 31 Jul 2022, Accepted 22 Feb 2023, Published online: 07 Mar 2023

References

  • Armaghani, D. J., M. Koopialipoor, A. Marto, and S. Yagiz. 2019. “Application of Several Optimization Techniques for Estimating TBM Advance Rate in Granitic Rocks.” Journal of Rock Mechanics and Geotechnical Engineering 11 (4): 779–789. doi:10.1016/j.jrmge.2019.01.002
  • Barton, N. 1999. “TBM Performance Estimation in Rock Using QTBM.” Tunnels and Tunnelling International 31 (9): 30–34. https://www.researchgate.net/publication/313079833_TBM_performance_in_rock_using_QTBM.
  • Chen, X., X. L. Liu, E. Z. Wang, and S. J. Wang. 2021. “Prediction of Tunnel Boring Machine Operating Parameters Using Various Machine Learning Algorithms.” Tunnelling and Underground Space Technology 109 (5): 103699. doi:10.1016/j.tust.2020.103699.
  • Du, Y. L., and L. J. Du. 2011. Full Face Hard Rock Tunnel Boring Machine - System Principle and Integrated Design. Wuhan: Huazhong University of Science and Technology Press.
  • Goh, A. T. C., W. Zhang, Y. Zhang, Y. Xiao, and Y. Xiang. 2018. “Determination of Earth Pressure Balance Tunnel-Related Maximum Surface Settlement: A Multivariate Adaptive Regression Splines Approach.” Bulletin of Engineering Geology and the Environment 77 (2): 489–500. doi:10.1007/s10064-016-0937-8.
  • Gregor, K., I. Danihelka, A. Graves, D. J. Rezende, and D. Wierstra. 2015. “Draw: A Recurrent Neural Network for Image Generation.” Proceedings of Machine Learning Research 37: 1462–1471. doi:10.48550/arXiv.1502.04623.
  • Grima, M. A., P. A. Bruines, and P. N. W. Verhoef. 2000. “Modeling Tunnel Boring Machine Performance by Neuro-Fuzzy Methods.” Tunnelling and Underground Space Technology 15 (3): 259–269. doi:10.1016/S0886-7798(00)00055-9.
  • Guan, Z., T. Deng, S. Z. Du, B. Li, and L. Jiang. 2012. “Markovian Geology Prediction Approach and its Application in Mountain Tunnels.” Tunnelling and Underground Space Technology 31 (5): 61–67. doi:10.1016/j.tust.2012.04.007.
  • Guo, D., J. Li, S. H. Jiang, L. X, and Z. Y. Chen. 2022. “Intelligent Assistant Driving Method for Tunnel Boring Machine Based on Big Data.” Acta Geotechnica 17 (4): 1019–1030. doi:10.1007/s11440-021-01327-1.
  • Jing, X., Y. M. Xia, Z. Y. Ji, and X. W. Zhou. 2019. “Soft Rock Cutting Mechanics Model of TBM Cutter and Experimental Research.” ICIRA Intelligent Robotics and Applications 5928: 383–391. doi:10.1007/978-3-642-10817-4_38.
  • Jing, L. J., N. Zhang, C. Yang, and X. Ju. 2018. “A Design Method Research on TBM Face Cutter Spacing Layout Based on Mnimum Specific Energy.” Tiedao Xuebao/Journal of the China Railway Society 40 (12): 123–129. doi:10.3969/j.issn.1001-8360.2018.12.016.
  • LeCun, Y., B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. 1989. “Backpropagation Applied to Handwritten zip Code Recognition.” Neural Computation 1 (4): 541–551. doi:10.1162/neco.1989.1.4.541.
  • Li, J., P. Li, D. Guo, X. Li, and Z. Y. Chen. 2021. “Advanced Prediction of Tunnel Boring Machine Performance Based on Big Data.” Geoscience Frontiers 12 (1): 331–338. doi:10.1016/j.gsf.2020.02.011.
  • Li, X., M. Yao, J. D. Yuan, Y. J. Wang, and P. Y. Li. 2022. “Deep Learning Characterization of Rock Conditions Based on Tunnel Boring Machine Data.” Underground Space. (Accepted).
  • Liu, D. S., H. L. Liu, Y. Wu, W. G. Zhang, Y. L. Wang, and M. Santosh. 2022. “Characterization of geo-Material Parameters: Gene Concept and big Data Approach in Geotechnical Engineering.” Geosystems and Geoenvironment 1 (1): 100003. doi:10.1016/j.geogeo.2021.09.003.
  • Liu, Q. S., X. Y. Wang, X. Huang, and X. Yin. 2020. “Prediction Model of Rock Mass Class Using Classification and Regression Tree Integrated AdaBoost Algorithm Based on TBM Driving Data.” Tunnelling and Underground Space Technology Incorporating Trenchless Technology Research 106: 103595. doi:10.1016/j.tust.2020.103595.
  • National standards compilation group of People's Republic of China. GB/T 34652-2017. 2017. Full Face Tunnel Boring Machine—Open Type Hard Rock Tunnel Boring Machine. Beijing: China Quality Inspection Press.
  • Ozdemir, L. 1977. “Development of Theoretical Equations for Predicting Tunnel Boring Ability.” Ph.D. thesis. Colorado School of Mines.
  • Phoon, K. K., and W. G. Zhang. 2022. “Future of Machine Learning in Geotechnics.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 15: 1–16. doi:10.1080/17499518.2022.2087884.
  • Shelhamer, E., J. Long, and T. Darrell. 2017. “Fully Convolutional Networks for Semantic Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (4): 640–651. doi:10.1109/TPAMI.2016.2572683.
  • Teale, R. 1965. “The Concept of Specific Energy in Rock Drilling.” International Journal of Rock Mechanics and Mining Science & Geomechanics Abstracts 2 (1): 57–73. doi:10.1016/0148-9062(65)90022-7.
  • Wang, H. J., L. M. Zhang, H. Y. Luo, J. He, and R. W. M. Cheung. 2021a. “AI-powered Landslide Susceptibility Assessment in Hong Kong.” Engineering Geology 288: 106103. doi:10.1016/j.enggeo.2021.106103.
  • Wang, X., H. H. Zhu, M. Q. Zhang, and L. Y. Zhang. 2021b. “An Integrated Parameter Prediction Framework for Intelligent TBM Excavation in Hard Rock.” Tunnelling and Underground Space Technology Incorporating Trenchless Technology Research 118: 104196. doi:10.1016/J.TUST.2021.104196.
  • Xu, W. H., Y. F. Kang, L. C. Chen, L. Q. Wang, C. B. Qin, L. T. Zhang, D. Liang, C. Z. Wu, and W. G. Zhang. 2022. “Dynamic Assessment of Slope Stability Based on Multi-Source Monitoring Data and Ensemble Learning Approaches: A Case Study of Jiuxianping Landslide.” Geological Journal, 1–19. doi:10.1002/gj.4605.
  • Xu, H., J. Zhou, G. Asteris, P. Jahed Armaghani, D. Tahir, and M. M. 2019. “Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate.” Applied Sciences 9 (18): 3715. doi:10.3390/app9183715
  • Yagiz, S. 2002. “Development of Rock Fracture and Brittleness Indices to Quantify the Effects of Rock Mass Features and Toughness in the CSM Model Basic Penetration for Hard Rock Tunneling Machines.” Ph.D. thesis. Colorado School of Mines.
  • Yagiz, S. 2008. “Utilizing Rock Mass Properties for Predicting TBM Performance in Hard Rock Condition.” Tunnelling and Underground Space Technology 23 (3): 326–339. doi:10.1016/j.tust.2007.04.011.
  • Yagiz, S., and H. Karahan. 2011. “Prediction of Hard Rock TBM Penetration Rate Using Particle Swarm Optimization.” International Journal of Rock Mechanics and Mining Sciences 48 (3): 427–433. doi:10.1016/j.ijrmms.2011.02.013.
  • Yao, Y., Y. Wei, and F. X. Gao. 2006. “Anomaly Intrusion Detection Approach Using Hybrid MLP/CNN Neural Network.” In Sixth International Conference on Intelligent Systems Design and Applications 2: 1095–1102. doi:10.1109/ISDA.2006.253765.
  • Zeiler, M. D., and R. Fergus. 2014. “Visualizing and Under-standing Convolutional Networks.” In Computer Vision(ECCV 2014), Lecture Notes in Computer Science. Vol. 8689, 818–833. Berlin/Heidelberg, Germany: Springer. doi:10.1007/978-3-319-10590-1_53.
  • Zhang, W. G., Y. Q. Li, C. Z. Wu, H. R. Li, ATC Goh, and H. L. Liu. 2022. “Prediction of Lining Response for Twin-Tunnel Construction in Anisotropic Clays Using Machine Learning Techniques.” Journal of Underground Space 7: 122–133. doi:10.1016/j.undsp.2020.02.007.
  • Zhang, W. G., H. R. Li, C. Z. Wu, Y. Q. Li, Z. Q. Liu, and H. L. Liu. 2021. “Soft Computing Approach for Prediction of Surface Settlement Induced by Earth Pressure Balance Shield Tunneling.” Journal of Underground Space 6 (4): 353–363. doi:10.1016/j.undsp.2019.12.003.
  • Zhao, Z., Q. Gong, Y. Zhang, and J. Zhao. 2007. “Prediction Model of Tunnel Boring Machine Performance by Ensemble Neural Networks.” Geomechanics and Geoengineering: An International Journal 2 (2): 123–128. doi:10.1080/17486020701377140.

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